How to work with a selection from Mongodb in order to avoid unnecessary calls to the database?

Question

Python

Good morning.
Situation: I get a number of records from Mongodb according to the parameters I need, for example:

tasks_query = task_base.find({
    "$and": [{
        "order_id": x
    }, {
        "site": y
    }, {
        "category": z
    }]
})

Then I need to cycle through this data several thousand times in a loop, making a selection from the Cursor according to a certain parameter, figuratively:

for task in task_query:
    if task['order_id'] = 234:
        return task['category']

The bottom line: I do not want to make several thousand calls to the database, but I want to get one large sample into memory, and then iterate over it. But:

• If I turn the selection from the base into a list of dictionaries, I will have to check the entire selection for loops each time to find the right one, which is very time consuming and resource intensive
  
• When converted into a dictionary, it will be + - the same, because the indices in different samples are different.
  

Question: if I just save the resulting Mongo Cursor to a variable, can I select from it through find () as from a regular database, without having to access the database every time?
If there is some other solution (for example, another data structure that supports indexes) or I missed something important, I will be grateful for any advice :)

Answer 1

Mongo is incredibly fast to read. Let it be she who is engaged in searching and sorting, and not python; that's what it's designed for. Don't be afraid to load it with requests.
Finally, make a very simple test. Take the time and describe the required functionality in both versions: monga and python. Measure running time. And everything will fall into place.

Answer 2

Then I need to go through the cycles several thousand times ...

It makes no sense, because the result will be only one order_id.

When converted to a dictionary, it will be + - the same

One dictionary - one index, if you need several indexes - make several dictionaries. This is the fastest - O(1) if everything fits into memory. You don't need a special person for this.
But I don't think that it's critical for you in terms of speed, so it's better not to worry and use mongo as it is, it's fast enough.